Automatic Moving Object Extraction for Content-Based Applications

Xu, Haifeng; Younis, Akmal A.; Kabuka, Mansur R.

doi:10.1109/tcsvt.2004.828338

Cited by 41 publications

(22 citation statements)

References 47 publications

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“…These properties suggest that this particle video approach potentially can estimate long-range coherent motion patterns, while representing details of regional motion by adapting the granularity locally. These properties are advantageous in video coding, since data redundancy can be explored in sets of frames that are not immediate neighbors, not only in pairs of adjacent frames, as in many other approaches [28], [35], [36].…”

Section: Estimation Of Particle Trajectoriesmentioning

confidence: 99%

Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Silva

Scharcanski

2010

IEEE Trans. on Image Process.

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Abstract-This work describes an approach for object-oriented video segmentation based on motion coherence. Using a tracking process based on adaptively sampled points (namely, particles), 2-D motion patterns are identified with an ensemble clustering approach. Particles are clustered to obtain a pixel-wise segmentation in space and time domains. The segmentation result is mapped to an image spatio-temporal feature space. Thus, the different constituent parts of the scene that move coherently along the video sequence are mapped to volumes in this spatio-temporal space. These volumes make the redundancy in the temporal sense more explicit, leading to potential gains in video coding applications. The proposed solution is robust and more generic than similar approaches for 2-D video segmentation found in the literature. In order to illustrate the potential advantages of using the proposed motion segmentation approach in video coding applications, the PSNR of the temporal predictions and the entropies of prediction errors obtained in our experiments are presented, and compared with other methods. Our experiments with real and synthetic sequences suggest that our method also could be used in other image processing and computer vision tasks, besides video coding, such as video information retrieval and video understanding.

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Section: Estimation Of Particle Trajectoriesmentioning

confidence: 99%

Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Silva

Scharcanski

2010

IEEE Trans. on Image Process.

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“…Shamim's algorithm is an example of this kind of algorithm [27]; however, the computational complexity is very high. Finally, many algorithms use a hybrid approach with the above concepts, such as Xu's algorithm [35]. The core operations of these algorithm are shown in Table 1.…”

Section: Analysis Of Existing Algorithmsmentioning

confidence: 99%

“…Ours [7] Gradient, change detection, background registration, post-processing Kim [13] Change detection, watershed Mech [19] Change detection, relaxation, optical flow, edge detection, edge fitting Meier [20] Morphological motion filter, optical flow, Canny edge detection, Hausdorff distance Kim [12] Canny edge detection, morphological operation Wang [32] Multiscale gradient, watershed, motion tacking and projection Tsaig [30] Watershed, hierarchical region matching Tsai [29] 3-D watershed, Bayesian volume merging Shamim [27] Global-to-local motion segmentation, morphology operations Xu [35] Motion segmentation, Canny edge detection, Hausdorff distance, watershed [23]. Motion estimation related operations generate motion field information with motion estimation algorithms.…”

Section: Algorithmmentioning

confidence: 99%

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Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation

Chien

Chen

2008

J Sign Process Syst

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Video object segmentation is an important pre-processing task for many video analysis systems. To achieve the requirement of real-time video analysis, hardware acceleration is required. In this paper, after analyzing existing video object segmentation algorithms, it is found that most of the core operations can be implemented with simple morphology operations. Therefore, with the concepts of morphological image processing element array and stream processing, a reconfigurable morphological image processing accelerator is proposed, where by the proposed instruction set, the operation of each processing element can be controlled, and the interconnection between processing elements can also be reconfigured. Simulation results show that most of the core operations of video object segmentation can be supported by the accelerator by only changing the instructions. A prototype chip is designed to support real-time change-detection-andbackground-registration based video object segmentation algorithm. This chip incorporates eight macro processing elements and can support a processing capacity of 6,200 9-bit morphological operations per second on a SIF image. Furthermore, with the proposed tiling and pipelined-parallel techniques, a realtime watershed transform can be achieved using 32 macro processing elements.

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“…For object segmentation in video sequences, temporal information in addition to spatial information can be effectively used for efficient extraction of objects [3][4][11][12]. Spatial information can be used to obtain boundaries of objects while temporal information is used to track the motion of the objects.…”

Section: Introductionmentioning

confidence: 99%

Block-Based Predictive Watershed Transform for Parallel Video Segmentation

Jang¹,

Lee²

2012

JSTS:Journal of Semiconductor Technology and Science

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Abstract-Predictive watershed transform is a popular object segmentation algorithm which achieves a speed-up by identifying image regions that are different from the previous frame and performing object segmentation only for those regions. However, incorrect segmentation is often generated by the predictive watershed transform which uses only local information in merge-split decision on boundary regions. This paper improves the predictive watershed transform to increase the accuracy of segmentation results by using the additional information about the root of boundary regions. Furthermore, the proposed algorithm is processed in a block-based manner such that an image frame is decomposed into blocks and each block is processed independently of the other blocks. The block-based approach makes it easy to implement the algorithm in hardware and also permits an extension for parallel execution. Experimental results show that the proposed watershed transform produces more accurate segmentation results than the predictive watershed transform.

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Automatic Moving Object Extraction for Content-Based Applications

Cited by 41 publications

References 47 publications

Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Video Segmentation Based on Motion Coherence of Particles in a Video Sequence

Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation

Block-Based Predictive Watershed Transform for Parallel Video Segmentation

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